IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v10y2021i5p505-d550987.html
   My bibliography  Save this article

Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Author

Listed:
  • Gregoriy Kaplan

    (Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization-Volcani Institute, HaMaccabim Road 68, P.O.B 15159, Rishon LeZion 7528809, Israel)

  • Offer Rozenstein

    (Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization-Volcani Institute, HaMaccabim Road 68, P.O.B 15159, Rishon LeZion 7528809, Israel)

Abstract

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R 2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R 2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R 2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R 2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

Suggested Citation

  • Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:505-:d:550987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/10/5/505/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/10/5/505/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    2. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2018. "Estimating cotton water consumption using a time series of Sentinel-2 imagery," Agricultural Water Management, Elsevier, vol. 207(C), pages 44-52.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    2. Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    3. Xianglong Fan & Xin Lv & Pan Gao & Lifu Zhang & Ze Zhang & Qiang Zhang & Yiru Ma & Xiang Yi & Caixia Yin & Lulu Ma, 2022. "Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum," Land, MDPI, vol. 12(1), pages 1-19, December.
    4. Linker, Raphael & Kisekka, Isaya, 2022. "Concurrent data assimilation and model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 274(C).
    5. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    2. Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    3. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    4. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    5. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    6. Mahmoud, Shereif H. & Gan, Thian Yew, 2019. "Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data," Agricultural Water Management, Elsevier, vol. 212(C), pages 35-47.
    7. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    8. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    9. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    10. Jun Ma & Jianpeng Zhang & Jinliang Wang & Vadim Khromykh & Jie Li & Xuzheng Zhong, 2023. "Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    11. De Caro, Dario & Ippolito, Matteo & Cannarozzo, Marcella & Provenzano, Giuseppe & Ciraolo, Giuseppe, 2023. "Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using gr," Agricultural Water Management, Elsevier, vol. 290(C).
    12. Munitz, Sarel & Schwartz, Amnon & Netzer, Yishai, 2019. "Water consumption, crop coefficient and leaf area relations of a Vitis vinifera cv. 'Cabernet Sauvignon' vineyard," Agricultural Water Management, Elsevier, vol. 219(C), pages 86-94.
    13. Eliav Shtull-Trauring & Asher Azenkot & Nirit Bernstein, 2022. "Translational Platform for Increasing Water Use Efficiency in Agriculture: Comparative Analysis of Plantation Crops," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 571-587, January.
    14. French, Andrew N. & Hunsaker, Douglas J. & Sanchez, Charles A. & Saber, Mazin & Gonzalez, Juan Roberto & Anderson, Ray, 2020. "Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest," Agricultural Water Management, Elsevier, vol. 239(C).
    15. Pereira, L.S. & Paredes, P. & López-Urrea, R. & Hunsaker, D.J. & Mota, M. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for vegetable crops, an update of FAO56 crop water requirements approach," Agricultural Water Management, Elsevier, vol. 243(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:10:y:2021:i:5:p:505-:d:550987. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.